A substance of interest, also termed an analyte, may be measured directly or, as is more often the case, indirectly. For indirect measurements a more readily detectable substance is formed by reacting the substance of interest with one or more reagents. The level of the more readily detectable substance is measured at a defined time after the initiation of the reaction. This level is converted into the level of the substance of interest by way of a calibration curve.
Calibration is a basic requirement for quantitative estimation of an analyte or substance—whether for dispensing or measurement. This generates a composite calibration curve to reduce errors. For instance, a clinical diagnostic analyzer or a point of care instrument measures analytes with the aid of a calibration curve—which curve is sometimes also referred to as a dose-response curve.
A clinical diagnostic analyzer is a complex machine with that is capable of both high accuracy and throughput. It is typically operated using software routines to both process samples and to detect errors in such processing or other errors—for instance in the samples being analyzed or a failure in its subsystems. FIG. 1 shows a clinical diagnostic analyzer 100 with four major component sub-systems or parts. It has a Reagent Management Center 110, a Sample Handling Center 120, a Supply Center 130 and a Processing Center 140. These subsystems are typically coordinated by a Scheduler—typically implemented with the aid of software—that specifies the particular operations to be performed by the clinical diagnostic analyzer subsystems on particular samples or reagents at specified time points. To aid in this task, the clinical diagnostic analyzer relies on a clock signal.
Analyte concentrations in a sample can typically be quantitatively detected by reading a signal during a short time window, for instance, in the clinical diagnostic analyzer of FIG. 1, in the Processing Center 140. Then, the detected signal strength is converted to analyte concentration. This conversion typically uses a single calibration curve. The time window may be short with tight tolerances for greater accuracy or with more forgiving specifications if the resultant errors are acceptable.
As is known in the prior art, a calibration curve is prepared using known analyte concentrations, and interpolation—and limited extrapolation—to allow calculation of analyte concentrations for substantially all signal strengths in the measurement range. Extending the range of measurements for analytes of interest has been a longstanding challenge. For instance, US Patent Publication No. 2007/0259450 describes a method for improving the range of an analyte test using special reagents. Another method for extending the range of measurements is provided by U.S. Pat. No. 7,829,347, which describes a method for detecting a region where the hook effect may be encountered to allow implementation of corrective measures. Similarly, U.S. Pat. No. 7,054,759 describes an algorithm using multiple calibration curves to counter the ‘prozone phenomenon’ or the ‘prozone-like phenomenon’, which is encountered when increasing the analyte level does not result in an increase in an absorbance signal, but instead even leads to a decrease in the absorbance signal.
The accuracy of a calibration curve may vary with the signal strength. Thus, measured analyte concentrations may have different errors in different parts of the calibration curve. In particular, on one hand, towards the lower end, i.e., with regard to the lower analyte detection limit, the measuring accuracy is limited by the low signal strength due to, for instance, the affinity and selectivity of the binding partners (often antibodies) or the extent to which a reaction has progressed. Further limits are placed by the detection optics' sensitivity at the low end which may be limited by the labels used, as is seen in the detection of amplification products based on the Polymerase Chain Reaction (PCR). On the other hand, saturation effects limit the measurements towards the upper end, i.e., corresponding to high analyte concentrations. Thus, in the case of high concentrations or levels of an analyte in the sample flattening due to saturation or exhaustion of a reagent limits the accuracy of detection. For consistently accurate measurements using a calibration curve, the curve should be substantially linear relative to the analyte concentration over broad ranges of analyte concentrations. In other words the measured analyte concentration should be directly proportional to the corresponding signal strength, which also makes the curves intuitive.
This is not always possible or practical. Still, there have been many attempts at making calibration curves more useful and intuitive. The signal strength may be mathematically transformed to generate a linear relationship, for instance by using the logarithm of the signal (or analyte concentration) to span a large range. However, this is not useful for all tests of interest.
There have been many attempts to make the calibration curve more useful in various assays. An assay is a procedure to detect an analyte using techniques that include optical, immunological, affinity, amplification, activity and the like to generate a signal. Some example assays use more than one technique such as PCR based detection of very small amounts of nucleic acid material. An example of improving calibration curves used in assays is disclosed by U.S. Pat. No. 6,248,597, which describes a heterogeneous agglutination immunoassay based on light scattering in which the dynamic measuring range is extended by particles differing in their light scattering properties. Binding partners having a high affinity for the analyte are immobilized on the particles which cause a large light scattering. In contrast, binding partners having a low affinity for the analyte are immobilized on the particles which exhibit low light scattering. This technique makes detection at low levels more sensitive while staving off saturation at high levels.
Another method is disclosed by U.S. Pat. No. 5,585,241. In order to increase the dynamic measuring range, it proposes in connection with a flow cytometry immunoassay that two particles of different sizes are loaded with two antibodies having different affinities for the same antigen (small particles loaded with high-affinity antibody, large particles loaded with low-affinity antibody).
A similar strategy is disclosed by U.S. Pat. No. 4,595,661, which uses a double standard curve, one from each particle type. Thus, the measurement of the sum of the contributions from the two binding reactions taking place in a mixed system. The low affinity antibody makes a significant contribution at high ligand concentrations while high sensitivity continues to be provided by the high-affinity antibody at low concentrations of the analyte. Each sample measurement therefore results in two measurement values—one for each particle size—and the two values must fit as a pair to the double standard curve for the analyte concentration in question.
U.S. Pat. No. 5,073,484 discloses that an immunologically detectable analyte can be quantitatively detected using several discrete, successive binding zones in a flow-through system. The number of zones in which the specific binding and detection reactions take place increases with an increasing amount of analyte in the sample. The number of zones in which analyte generates a signal correlates with the amount of analyte in the sample. Further, the number of binding zones can be increased in order to extend the measuring range. A disadvantage of this is that an automatic evaluation of the binding zones requires a complicated optical system which is able under certain circumstances to simultaneously detect and evaluate a large number of zones in order to thus allow a quantitative analyte determination.
Diagnostic instruments typically use a calibration curve based on a set of instrument responses to known sample values and fit to conform to a mathematical relationship—such as linear, quadratic, exponential, logarithmic and the like. This calibration curve, also known as a dose-response curve, is then read to determine values corresponding to an unknown sample. This curve allows instrument responses to unknown sample to be combined with the calibration curve to generate a value for the unknown sample.
The calibration curve itself often limits the useful measuring range of a test. This follows from the calibration curve shape, which while desired to be linear with an appreciable slope, is often inconveniently non-linear or too flat to provide adequate discrimination. Test values in such regions are difficult to pin down since small differences in sample strength may result in large changes in signal strength or large changes in sample strength correspond to even no appreciable change in signal strength. For example with diagnostic tests, these variables drive the performance of the test by relating predicted concentrations to well known performance characteristics such as linearity and limit of quantitation (see CLSI Guidelines EP6-A and EP17-A). The limitation in test performance can be limited by both (i) the actual flattening of response versus concentration below an acceptable threshold limit where no mathematical modeling can be helpful, and (ii) the failure of the mathematical model to adequately fit the actual response data.
An example of the difficulties due to flattening of response versus concentration is shown in FIG. 2. In this example, the dose-response curve is relatively flat between 0-0.5 au and between 3.2-6 au. The essential useful measuring range of the response function is between 0.5-3.2 au (between the broken lines), independent of the mathematical model used to fit the response data. Beyond this range, the flatness of the curve causes a poor correlation between response and concentration due to the imprecision of the measured response.
An example of a failure of the mathematical model to adequately fit the actual response data is shown in FIG. 3. In this example, the fitted calibration curve using a Logit/Log 4 function (broken line) does not fit the actual calibrator response data (inversely proportional dose-response curve). There is a small fitting deviation at low concentration where the fitted calibration curve flattens out the response. At high concentrations, there is a significant deviation between the fitted calibration curve and the calibration response data due to the flattening of the calibration curve at concentration greater than 1.5 au. Because of the lack of mathematical fit, the useful measuring range would be much smaller than the 0.02-2.7 range the calibrator response curve shape data suggests. In this example, the useful measuring range would be reduced to approximately 0.15-0.75 au (between the vertical broken lines) due to the lack of fit of the calibration model at both the low and high concentration regions.
In cases when the useful measuring range is limited by the calibration curve or fitting model, one would like to be able to generate useful dose-response curves that span greater than the approximate 0.5-3.2 au measuring range shown in FIG. 2. Expanding the measuring range without the having to run a second experiment—that uses additional reagents and sample—remains an unmet need.